code: def __init__(self, data_dir=None, is_train=True, transforms=None):
      self.imgs_path = [
          os.path.join(data_dir, n) for n in os.listdir(data_dir)
      ]
      if is_train:
          self.imgs_path = self.imgs_path[:60000]
      else:
          self.imgs_path = self.imgs_path[60000:]
      self.transforms = transforms
  
imports: import argparse
  import logging
  import os
  import time
  import hostlist
  import torch
  import torch.nn
  from PIL import Image
  from torch.utils.data import Dataset
  from torchvision import transforms
  from pythae.data.datasets import DatasetOutput
  from pythae.models import VQVAE, VQVAEConfig
  from pythae.models.base.base_utils import ModelOutput
  from pythae.models.nn.base_architectures import BaseDecoder, BaseEncoder
  from pythae.models.nn.benchmarks.utils import ResBlock
  from pythae.trainers import BaseTrainer, BaseTrainerConfig
label_desc: Use image node_id 1 to create a new FFHQ object from inherited base classes: Dataset with example: obj = FFHQ(data_dir, is_train, transforms)